Updated: 2020-08-02 08:31:50 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

This is the same computation at a County level


state R_e cases daily_cases
Rhode Island 1.33 17322 108
Missouri 1.25 45996 1382
Montana 1.25 4041 143
Massachusetts 1.24 117643 418
Oklahoma 1.22 37914 1162
South Dakota 1.20 8681 84
Connecticut 1.16 49678 172
Nebraska 1.16 26349 323
Tennessee 1.16 105661 2607
Georgia 1.13 172743 3781
Illinois 1.13 180926 1607
Michigan 1.13 91503 840
New Jersey 1.13 183331 439
West Virginia 1.13 6746 147
Kentucky 1.11 31678 694
Maryland 1.11 89856 964
Mississippi 1.11 60174 1450
North Dakota 1.09 6564 130
Oregon 1.09 18871 359
Arkansas 1.07 41816 793
Iowa 1.07 45363 532
Maine 1.07 3937 22
Minnesota 1.07 55146 741
Ohio 1.07 92398 1430
Texas 1.06 449230 9055
Wisconsin 1.06 54211 944
Indiana 1.05 68793 878
North Carolina 1.05 124357 1974
Arizona 1.04 176718 2656
Idaho 1.04 21363 516
Wyoming 1.04 2776 50
Nevada 1.03 49250 1092
New Hampshire 1.02 6621 31
Pennsylvania 1.02 117732 945
Alabama 1.01 89464 1758
South Carolina 1.01 90728 1625
New York 1.00 420474 680
Virginia 1.00 72819 798
Washington 1.00 59746 843
New Mexico 0.99 20948 301
Florida 0.98 481006 10050
Colorado 0.97 47487 544
Kansas 0.97 28189 407
California 0.95 511829 8765
Delaware 0.92 14671 92
Utah 0.88 40775 471
Louisiana 0.82 117603 1610
Vermont 0.65 1422 4

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 row(s) containing missing values (geom_path).

Mortality Trend

National \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Trend

Distribution of \(R_e\) Values

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Pierce WA 4 1 1.1 5453 630 110
Stevens WA 28 2 1.6 90 200 6
King WA 1 3 1.0 15417 710 168
Snohomish WA 3 4 1.0 5783 740 69
Chelan WA 10 5 1.1 1098 1450 33
Spokane WA 5 6 1.0 3780 760 80
Douglas WA 13 7 1.1 781 1890 25
Benton WA 6 8 1.0 3688 1900 54
Yakima WA 2 9 0.9 10532 4220 73
Franklin WA 7 10 1.0 3428 3780 43
Grant WA 9 12 1.0 1300 1370 25
Clark WA 8 18 0.8 1794 390 21
OR
county ST case rank severity R_e cases cases/100k daily cases
Yamhill OR 12 1 1.6 347 330 13
Multnomah OR 1 2 1.0 4413 550 77
Jackson OR 11 3 1.4 367 170 14
Washington OR 2 4 1.1 2789 480 50
Umatilla OR 4 5 1.0 2008 2610 50
Malheur OR 6 6 1.2 663 2180 16
Benton OR 20 7 1.5 154 170 4
Marion OR 3 9 1.0 2615 780 36
Lane OR 8 10 1.1 510 140 11
Clackamas OR 5 11 1.0 1380 340 19
Deschutes OR 7 12 0.9 535 300 14
Lincoln OR 9 17 1.0 392 820 2
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.1 191064 1890 2876
Lassen CA 36 2 1.7 636 2040 16
Merced CA 23 3 1.3 4375 1630 172
Fresno CA 7 4 1.1 15137 1550 403
San Francisco CA 17 5 1.3 6694 770 146
Sonoma CA 25 6 1.4 3026 600 88
Amador CA 48 7 1.7 128 340 10
San Bernardino CA 4 8 0.9 33110 1550 637
Alameda CA 9 9 1.1 11508 700 193
San Diego CA 5 10 0.9 29652 900 426
Riverside CA 2 15 0.8 37885 1590 471
Orange CA 3 16 0.8 36775 1160 436
Kern CA 6 17 0.7 20610 2330 627
San Joaquin CA 8 26 0.7 11721 1600 194

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 119083 2800 1891
Pima AZ 2 2 1.2 16424 1610 271
Pinal AZ 4 3 1.0 8129 1940 122
Yuma AZ 3 4 1.0 11071 5330 134
Yavapai AZ 10 5 1.1 1788 800 36
Gila AZ 12 6 1.2 824 1540 25
Cochise AZ 11 7 1.2 1522 1210 25
Apache AZ 6 9 1.0 3032 4240 22
Mohave AZ 7 10 0.8 3012 1460 45
Navajo AZ 5 11 0.8 5242 4820 30
Coconino AZ 8 12 0.8 2968 2120 19
Santa Cruz AZ 9 13 0.8 2615 5610 18
CO
county ST case rank severity R_e cases cases/100k daily cases
Adams CO 3 1 1.1 6028 1210 79
Summit CO 16 2 1.6 337 1110 4
Arapahoe CO 2 3 1.0 6949 1090 71
El Paso CO 4 4 1.0 4653 680 80
Denver CO 1 5 0.9 9732 1400 91
Jefferson CO 5 6 1.0 3926 690 48
Boulder CO 7 7 1.1 1907 590 21
Larimer CO 9 9 0.9 1381 410 22
Douglas CO 8 12 0.8 1636 500 20
Weld CO 6 14 0.8 3538 1200 20
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 0.9 19305 1720 200
Utah UT 2 2 0.9 7818 1320 106
Weber UT 4 3 0.9 2571 1040 42
Davis UT 3 4 0.9 2958 870 44
Iron UT 11 5 1.0 514 1030 7
Washington UT 5 6 0.7 2302 1430 24
Box Elder UT 12 7 1.0 324 610 5
Cache UT 6 8 0.8 1816 1480 10
San Juan UT 8 9 0.9 621 4060 7
Tooele UT 9 10 0.8 537 820 7
Summit UT 7 12 0.7 691 1710 4
NM
county ST case rank severity R_e cases cases/100k daily cases
Cibola NM 7 1 1.9 660 2450 54
Chaves NM 12 2 1.4 347 530 15
Curry NM 10 3 1.2 466 930 15
Bernalillo NM 1 4 0.8 4826 710 70
Santa Fe NM 9 5 1.1 578 390 14
San Juan NM 3 6 1.0 3005 2360 14
Doña Ana NM 4 7 0.8 2214 1030 31
Sandoval NM 6 8 1.0 1086 770 13
McKinley NM 2 10 0.8 4004 5500 16
Lea NM 8 11 0.8 635 910 17
Otero NM 5 17 0.5 1086 1650 5

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Cumberland NJ 15 1 1.5 3239 2110 17
Burlington NJ 12 2 1.3 5853 1310 36
Hudson NJ 3 3 1.3 19663 2940 23
Monmouth NJ 8 4 1.2 10193 1640 39
Passaic NJ 5 5 1.3 17606 3490 27
Camden NJ 9 6 1.2 8398 1660 46
Essex NJ 2 7 1.2 19758 2490 35
Bergen NJ 1 8 1.1 20759 2230 42
Ocean NJ 7 9 1.1 10515 1780 41
Middlesex NJ 4 15 0.7 17911 2170 28
Union NJ 6 16 0.9 16745 3030 7
PA
county ST case rank severity R_e cases cases/100k daily cases
Union PA 42 1 2.1 150 330 7
Fayette PA 29 2 1.5 398 300 16
Allegheny PA 4 3 1.0 8193 670 154
Lancaster PA 6 4 1.2 5576 1040 49
Luzerne PA 11 5 1.4 3277 1030 22
Delaware PA 3 6 1.1 8781 1560 75
Erie PA 21 7 1.4 943 340 17
Philadelphia PA 1 8 0.9 30468 1930 145
Montgomery PA 2 9 1.0 9824 1200 54
Bucks PA 5 11 1.0 6949 1110 46
Lehigh PA 9 13 1.1 4836 1330 25
Chester PA 8 15 0.9 4917 950 42
Berks PA 7 18 0.9 5177 1240 25
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 1.2 11394 1850 180
Prince George’s MD 1 2 1.2 23029 2540 176
Baltimore MD 3 3 1.1 12028 1450 189
Montgomery MD 2 4 1.1 17696 1700 111
Calvert MD 15 5 1.5 603 660 13
Anne Arundel MD 5 6 1.1 6856 1210 80
Harford MD 9 7 1.1 1783 710 32
Howard MD 6 8 1.1 3570 1130 39
Charles MD 8 11 1.1 1858 1180 21
Frederick MD 7 16 0.8 3008 1210 18
VA
county ST case rank severity R_e cases cases/100k daily cases
Prince Edward VA 28 1 2.3 336 1460 16
Bedford VA 38 2 1.5 292 370 12
Henry VA 24 3 1.5 494 960 11
Prince William VA 2 4 1.1 8907 1950 68
Norfolk city VA 7 5 1.1 3221 1310 88
Henrico VA 6 6 1.2 3607 1110 51
Virginia Beach city VA 4 7 0.9 4227 940 115
Chesterfield VA 5 9 1.1 3967 1170 42
Arlington VA 8 10 1.2 2908 1250 16
Fairfax VA 1 12 0.9 15709 1370 56
Loudoun VA 3 15 0.9 5033 1310 27
Newport News city VA 9 26 0.7 1603 890 27
WV
county ST case rank severity R_e cases cases/100k daily cases
Mercer WV 15 1 1.9 146 240 11
Logan WV 16 2 1.6 140 410 12
Raleigh WV 11 3 1.5 179 230 9
Grant WV 25 4 1.5 76 650 6
Kanawha WV 2 5 1.0 811 440 23
Monongalia WV 1 6 1.0 905 860 14
Cabell WV 4 7 1.1 326 340 8
Harrison WV 9 8 1.2 188 280 5
Berkeley WV 3 10 0.9 652 570 6
Ohio WV 6 18 0.5 253 590 3
Wood WV 7 20 0.7 236 280 2
Jefferson WV 5 22 0.5 289 510 1
Randolph WV 8 26 0.2 208 720 0
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.0 6856 1240 62
Sussex DE 2 2 0.7 5633 2570 19
Kent DE 3 3 0.8 2182 1250 12

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Mobile AL 2 1 1.2 8401 2030 220
Jackson AL 33 2 1.5 781 1500 25
Calhoun AL 14 3 1.3 1497 1300 70
Jefferson AL 1 4 1.0 11618 1760 224
Madison AL 4 5 0.9 4932 1380 107
Colbert AL 23 6 1.2 1074 1970 31
Cullman AL 22 7 1.2 1159 1410 30
Shelby AL 7 14 0.9 3116 1470 62
Montgomery AL 3 16 0.9 6208 2740 75
Tuscaloosa AL 5 18 0.9 3941 1910 58
Baldwin AL 6 20 0.8 3182 1530 80
Lee AL 9 22 0.9 2603 1630 38
Marshall AL 8 24 0.9 2919 3070 38
MS
county ST case rank severity R_e cases cases/100k daily cases
George MS 40 1 2.8 567 2390 77
Sharkey MS 77 2 2.0 164 3640 14
DeSoto MS 2 3 1.3 3162 1800 81
Tallahatchie MS 48 4 1.6 427 2970 17
Tunica MS 66 5 1.6 245 2410 12
Tishomingo MS 59 6 1.5 311 1600 17
Monroe MS 33 7 1.4 686 1910 19
Hinds MS 1 10 1.0 5220 2160 121
Forrest MS 8 14 1.1 1610 2130 38
Rankin MS 4 21 1.0 2141 1420 54
Jones MS 7 22 1.1 1740 2540 32
Harrison MS 5 23 1.0 2088 1030 51
Washington MS 9 31 0.9 1452 3080 34
Jackson MS 6 32 0.8 1907 1340 54
Madison MS 3 36 0.9 2290 2210 39
LA
county ST case rank severity R_e cases cases/100k daily cases
Calcasieu LA 4 1 1.0 6332 3160 161
East Baton Rouge LA 2 2 0.9 10843 2440 178
Caddo LA 6 3 1.0 6198 2500 94
Union LA 41 4 1.4 597 2660 10
Jefferson LA 1 5 0.8 14260 3280 127
Rapides LA 10 6 0.9 2978 2260 48
Ouachita LA 8 7 0.9 4415 2830 58
St. Tammany LA 7 8 0.8 4676 1850 64
Orleans LA 3 12 0.8 10312 2650 63
Lafayette LA 5 16 0.7 6231 2600 72
Tangipahoa LA 9 19 0.7 3081 2360 42

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Jefferson FL 63 1 2.5 275 1950 28
Marion FL 19 2 1.6 5162 1480 329
Gulf FL 58 3 2.0 367 2290 32
Miami-Dade FL 1 4 1.0 121771 4480 2965
Liberty FL 56 5 1.9 386 4610 12
Bradford FL 60 6 1.8 340 1260 20
Broward FL 2 7 1.0 56946 2980 1316
Palm Beach FL 3 10 1.0 33965 2350 617
Hillsborough FL 4 11 0.9 29623 2150 443
Pinellas FL 7 12 1.0 16595 1730 252
Orange FL 5 15 0.9 29520 2230 430
Polk FL 9 23 0.9 12762 1910 223
Lee FL 8 27 0.9 15562 2170 198
Duval FL 6 28 0.8 21394 2310 292
GA
county ST case rank severity R_e cases cases/100k daily cases
Chattahoochee GA 49 1 3.0 629 5840 27
Gwinnett GA 2 2 1.3 17454 1930 387
Richmond GA 9 3 1.4 3603 1790 145
Seminole GA 134 4 1.8 145 1720 10
Walton GA 42 5 1.5 932 1030 33
Hall GA 5 6 1.3 5462 2790 104
Fulton GA 1 7 1.0 17979 1760 368
DeKalb GA 3 12 1.1 12348 1660 214
Cobb GA 4 14 1.0 11435 1530 240
Muscogee GA 8 29 1.1 4270 2170 74
Chatham GA 6 39 0.9 4937 1720 113
Clayton GA 7 40 1.0 4494 1610 84

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Live Oak TX 118 1 3.1 214 1770 24
Taylor TX 35 2 2.4 1822 1340 139
Cameron TX 9 3 1.6 11058 2620 615
Maverick TX 36 4 1.8 1820 3140 98
Harris TX 1 5 1.2 74429 1620 1690
Zapata TX 134 6 2.2 158 1100 8
Scurry TX 88 7 2.0 437 2530 10
Nueces TX 8 8 1.3 12367 3430 368
Tarrant TX 4 13 1.1 28928 1430 611
Bexar TX 3 21 0.9 41492 2150 842
Dallas TX 2 23 0.9 50709 1960 683
Travis TX 5 26 1.0 20954 1740 242
El Paso TX 7 31 0.9 14437 1720 215
Hidalgo TX 6 49 0.6 17163 2020 267
OK
county ST case rank severity R_e cases cases/100k daily cases
Le Flore OK 30 1 2.2 230 460 22
Marshall OK 46 2 2.0 98 600 7
Cleveland OK 3 3 1.4 2655 960 103
Oklahoma OK 1 4 1.2 9221 1180 270
Tulsa OK 2 5 1.2 9038 1410 248
Sequoyah OK 29 6 1.6 251 610 20
Canadian OK 4 7 1.4 1059 770 40
Wagoner OK 9 8 1.3 721 930 26
Rogers OK 7 14 1.1 796 880 32
Comanche OK 8 24 1.0 775 630 15
Texas OK 5 39 1.0 1035 4900 2
McCurtain OK 6 46 0.5 826 2510 6

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Wayne MI 1 1 1.2 27240 1550 197
Macomb MI 3 2 1.3 9779 1130 117
Oakland MI 2 3 1.2 14722 1180 125
Shiawassee MI 28 4 1.8 324 470 4
Monroe MI 16 5 1.4 881 590 16
Kent MI 4 6 1.0 7124 1110 60
Kalamazoo MI 10 7 1.2 1535 590 19
Genesee MI 5 8 1.1 3485 850 33
Ottawa MI 9 12 1.1 1720 610 21
Washtenaw MI 6 16 1.0 2909 790 24
Saginaw MI 8 24 0.8 1850 960 18
Jackson MI 7 28 0.7 2414 1520 13
WI
county ST case rank severity R_e cases cases/100k daily cases
Barron WI 28 1 2.7 250 550 34
Wood WI 30 2 1.8 228 310 13
Walworth WI 8 3 1.4 1208 1170 31
Washington WI 11 4 1.3 867 640 29
Milwaukee WI 1 5 0.9 19549 2050 243
Waukesha WI 4 6 1.0 3606 900 109
Monroe WI 32 7 1.6 212 470 6
Dane WI 2 11 1.0 4118 780 50
Racine WI 5 12 1.0 3162 1620 46
Brown WI 3 19 1.0 3960 1520 34
Kenosha WI 6 20 0.9 2454 1460 34
Outagamie WI 9 23 1.0 1082 590 22
Rock WI 7 36 0.7 1493 920 12

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
St. Louis MN 20 1 1.7 398 200 15
Hennepin MN 1 2 1.0 17555 1420 222
McLeod MN 38 3 1.8 130 360 3
Dakota MN 3 4 1.1 3862 920 71
Ramsey MN 2 5 1.1 6776 1250 90
Crow Wing MN 29 6 1.5 198 310 8
Scott MN 9 7 1.2 1333 930 29
Anoka MN 4 8 1.0 3288 950 50
Washington MN 6 9 1.0 1855 730 32
Stearns MN 5 10 1.1 2803 1790 15
Olmsted MN 8 11 1.1 1604 1050 17
Nobles MN 7 40 0.8 1742 7980 2
SD
county ST case rank severity R_e cases cases/100k daily cases
Lincoln SD 4 1 1.4 559 1020 15
Minnehaha SD 1 2 1.2 4189 2240 30
Brown SD 5 3 1.5 404 1040 5
Union SD 6 4 1.2 196 1290 3
Pennington SD 2 5 0.8 818 750 6
Lyman SD 13 6 1.3 88 2270 1
Lake SD 16 7 0.8 83 660 2
Brookings SD 8 8 1.0 117 340 1
Clay SD 9 9 0.9 115 830 2
Codington SD 7 10 0.7 120 430 1
Beadle SD 3 12 0.5 588 3200 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Burleigh ND 2 1 1.1 953 1020 33
Stark ND 6 2 1.4 188 610 8
Benson ND 11 3 1.4 93 1350 8
Cass ND 1 4 0.9 2918 1680 20
Richland ND 12 5 1.1 92 560 4
Morton ND 4 6 0.9 273 890 8
Ward ND 7 7 0.9 180 260 6
Mountrail ND 8 9 1.0 112 1100 2
Williams ND 5 10 0.7 230 680 5
Grand Forks ND 3 11 0.6 626 890 5
Stutsman ND 9 12 0.7 110 520 2

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 1.1 17847 1890 66
Tolland CT 7 2 1.5 1024 680 10
Hartford CT 3 3 1.2 12667 1420 53
Windham CT 8 4 1.5 695 600 6
New London CT 5 5 1.3 1405 520 6
New Haven CT 2 6 1.0 13054 1520 23
Litchfield CT 4 7 1.0 1605 880 7
Middlesex CT 6 8 0.7 1380 840 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.4 21258 2680 69
Essex MA 3 2 1.4 17284 2210 58
Norfolk MA 5 3 1.3 10281 1470 56
Middlesex MA 1 4 1.2 25779 1620 80
Worcester MA 4 5 1.2 13360 1620 48
Bristol MA 7 6 1.2 9095 1630 37
Plymouth MA 6 7 1.2 9104 1780 21
Hampden MA 8 8 1.1 7432 1580 26
Barnstable MA 9 10 0.9 1759 820 10
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.3 14610 2300 88
Kent RI 2 2 1.4 1430 870 12
Newport RI 4 3 1.5 382 460 3
Bristol RI 5 4 1.2 306 630 2
Washington RI 3 5 1.1 593 470 3

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Ulster NY 13 1 1.9 2028 1130 18
New York City NY 1 2 0.9 230138 2730 284
Suffolk NY 2 3 1.1 43297 2910 68
Rensselaer NY 19 4 1.5 731 460 9
Dutchess NY 9 5 1.3 4499 1530 13
Nassau NY 3 6 1.1 43261 3190 51
Erie NY 7 7 1.1 8534 930 41
Orange NY 6 9 1.2 11106 2940 16
Westchester NY 4 10 1.0 35974 3710 34
Monroe NY 8 11 1.0 4738 640 25
Rockland NY 5 19 0.9 13875 4290 6

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.0 89 150 1
Franklin VT 2 2 0.5 118 240 0
Chittenden VT 1 3 0.2 719 440 1
Bennington VT 5 4 0.5 85 240 0
Windham VT 3 5 0.0 102 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.1 651 320 5
Cumberland ME 1 2 0.8 2048 700 6
Kennebec ME 4 3 0.9 167 140 2
Penobscot ME 5 4 0.9 146 100 1
Androscoggin ME 3 5 0.7 553 510 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Rockingham NH 2 1 1.2 1630 530 8
Hillsborough NH 1 2 0.9 3757 910 16
Cheshire NH 8 3 1.3 89 120 1
Strafford NH 4 4 0.8 329 260 2
Carroll NH 7 5 0.8 90 190 1
Merrimack NH 3 6 0.7 459 310 1
Belknap NH 5 7 0.7 106 170 1
Grafton NH 6 8 0.5 105 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Beaufort SC 8 1 1.2 3596 1970 107
Anderson SC 14 2 1.3 2018 1030 62
Richland SC 4 3 1.1 7877 1930 150
Aiken SC 16 4 1.3 1544 920 51
Florence SC 10 5 1.1 2938 2120 81
Georgetown SC 18 6 1.3 1268 2060 30
Darlington SC 21 7 1.2 1056 1570 34
Greenville SC 2 8 1.0 10149 2040 132
Horry SC 3 9 0.9 8072 2520 99
Charleston SC 1 10 0.9 11513 2920 142
Berkeley SC 6 15 0.9 3832 1830 62
Spartanburg SC 7 16 0.9 3698 1220 47
Lexington SC 5 21 0.8 4609 1610 66
York SC 9 23 0.9 3137 1210 52
NC
county ST case rank severity R_e cases cases/100k daily cases
McDowell NC 51 1 1.7 586 1300 26
Onslow NC 36 2 1.5 906 470 40
Davie NC 60 3 1.7 359 850 11
Nash NC 34 4 1.5 1016 1080 30
Ashe NC 84 5 1.8 119 440 6
Johnston NC 6 6 1.3 3013 1580 63
Mecklenburg NC 1 7 0.9 20734 1970 252
Wake NC 2 8 1.0 11084 1060 175
Cumberland NC 9 10 1.1 2696 810 68
Guilford NC 4 16 1.0 5178 990 78
Union NC 8 20 1.0 2768 1220 47
Durham NC 3 22 1.0 5839 1910 58
Forsyth NC 5 23 1.0 4886 1310 56
Gaston NC 7 25 1.0 2991 1380 55

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Big Horn MT 3 1 1.6 329 2460 22
Yellowstone MT 1 2 1.2 1049 660 33
Flathead MT 5 3 1.2 250 250 12
Missoula MT 4 4 1.2 259 220 9
Lewis and Clark MT 8 5 1.2 128 190 5
Lake MT 6 6 1.0 170 570 5
Gallatin MT 2 7 0.7 874 830 16
Cascade MT 7 8 1.0 147 180 6
Madison MT 9 9 1.0 76 920 3
WY
county ST case rank severity R_e cases cases/100k daily cases
Uinta WY 4 1 1.4 258 1250 4
Laramie WY 2 2 1.1 464 470 8
Teton WY 3 3 1.0 352 1530 11
Park WY 7 4 1.2 118 410 3
Natrona WY 6 5 0.9 220 270 3
Fremont WY 1 6 0.9 473 1180 4
Sweetwater WY 5 7 0.7 245 560 4
Campbell WY 8 8 0.8 118 250 2
Lincoln WY 9 11 0.1 92 480 0
ID
county ST case rank severity R_e cases cases/100k daily cases
Elmore ID 14 1 1.7 206 780 11
Bonneville ID 5 2 1.3 717 640 38
Ada ID 1 3 0.9 8009 1800 159
Kootenai ID 3 4 1.2 1580 1030 49
Bingham ID 13 5 1.4 227 500 11
Canyon ID 2 6 0.9 4821 2270 119
Twin Falls ID 4 7 1.2 1224 1460 26
Jerome ID 9 9 1.3 422 1800 7
Cassia ID 7 12 1.1 473 2000 10
Minidoka ID 8 16 0.8 434 2110 8
Blaine ID 6 21 0.8 570 2590 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Preble OH 57 1 2.0 158 380 8
Perry OH 75 2 1.9 101 280 6
Lucas OH 4 3 1.2 4760 1100 120
Ross OH 36 4 1.5 393 510 23
Fayette OH 76 5 1.8 91 320 4
Franklin OH 1 6 1.0 17018 1330 224
Scioto OH 52 7 1.6 185 240 10
Montgomery OH 5 9 1.1 3996 750 84
Cuyahoga OH 2 13 0.9 12530 1000 145
Hamilton OH 3 14 1.0 9050 1110 97
Summit OH 6 15 1.1 3228 600 54
Butler OH 8 20 1.0 2664 700 43
Mahoning OH 9 35 0.9 2390 1030 22
Marion OH 7 43 1.1 2867 4390 8
IL
county ST case rank severity R_e cases cases/100k daily cases
Bureau IL 46 1 2.1 130 390 12
Cook IL 1 2 1.1 106020 2030 625
Peoria IL 14 3 1.6 1280 690 53
Kane IL 4 4 1.3 9209 1730 76
Tazewell IL 26 5 1.6 353 260 16
Morgan IL 39 6 1.7 188 550 6
Will IL 5 7 1.1 8522 1240 72
Lake IL 2 9 1.0 11946 1700 88
Madison IL 9 14 1.1 2154 810 56
DuPage IL 3 15 1.0 11363 1220 86
St. Clair IL 6 16 1.0 3857 1460 66
McHenry IL 8 17 1.1 2955 960 39
Winnebago IL 7 27 1.0 3666 1280 26
IN
county ST case rank severity R_e cases cases/100k daily cases
Carroll IN 65 1 2.2 134 670 3
Marion IN 1 2 1.1 14720 1560 156
Delaware IN 26 3 1.5 626 540 13
Clark IN 13 4 1.3 1068 920 27
Decatur IN 40 5 1.6 333 1250 6
Madison IN 14 6 1.4 839 650 13
St. Joseph IN 5 7 1.1 3074 1140 49
Allen IN 4 9 1.1 3578 970 36
Vanderburgh IN 9 11 1.0 1676 920 44
Hamilton IN 6 12 1.0 2480 780 41
Lake IN 2 18 0.8 7043 1450 56
Elkhart IN 3 20 0.9 4649 2280 39
Hendricks IN 8 21 1.1 1749 1090 15
Cass IN 7 55 0.7 1751 4600 5

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Johnson TN 75 1 3.3 131 740 18
Lake TN 23 2 2.9 811 10780 20
Henry TN 66 3 2.1 200 620 21
Benton TN 84 4 2.2 88 550 10
Scott TN 81 5 2.2 99 450 10
Weakley TN 59 6 2.0 245 730 21
Madison TN 22 7 1.7 829 850 44
Shelby TN 2 8 1.2 21340 2280 451
Knox TN 5 14 1.1 4066 890 148
Sevier TN 9 20 1.2 1692 1760 56
Davidson TN 1 25 0.9 21446 3140 273
Rutherford TN 3 37 0.9 6013 1960 110
Hamilton TN 4 49 0.9 5595 1560 92
Williamson TN 6 56 0.9 3185 1460 51
Wilson TN 8 66 0.9 2042 1540 34
Sumner TN 7 69 0.8 3147 1750 44
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.1 7042 920 157
Fayette KY 2 2 1.2 3245 1020 78
Anderson KY 67 3 1.7 76 340 6
Clark KY 40 4 1.7 152 420 4
Christian KY 9 5 1.4 572 790 17
Graves KY 13 6 1.4 500 1340 13
Madison KY 15 7 1.4 377 420 13
Warren KY 3 15 0.9 2404 1900 33
Boone KY 5 22 1.0 1000 770 13
Shelby KY 6 24 1.0 703 1500 8
Daviess KY 7 25 1.0 703 700 10
Kenton KY 4 29 0.8 1295 790 18
Muhlenberg KY 8 57 0.6 630 2030 2

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Taney MO 18 1 1.9 412 750 33
Jackson MO 4 2 1.5 3402 490 139
St. Louis MO 1 3 1.2 13239 1330 395
Cooper MO 52 4 1.9 95 540 9
Ray MO 53 5 1.9 94 410 8
Marion MO 42 6 1.7 152 530 14
Clay MO 11 7 1.5 907 380 32
St. Louis city MO 2 8 1.2 4587 1470 110
Jefferson MO 5 10 1.2 1441 650 48
Boone MO 7 11 1.3 1216 690 32
St. Charles MO 3 14 1.0 3639 930 102
Greene MO 6 24 1.0 1265 440 38
Buchanan MO 9 40 1.0 1060 1190 7
Jasper MO 8 46 0.7 1215 1020 16
AR
county ST case rank severity R_e cases cases/100k daily cases
Chicot AR 20 1 2.3 546 5040 47
Greene AR 27 2 2.0 331 740 23
Little River AR 41 3 1.8 163 1310 14
Mississippi AR 18 4 1.6 725 1690 36
Independence AR 25 5 1.6 356 960 29
Sebastian AR 4 6 1.3 1727 1350 60
Boone AR 39 7 1.6 164 440 10
Hot Spring AR 5 8 1.6 1448 4320 9
Pulaski AR 2 12 0.9 4823 1230 80
Benton AR 3 14 0.9 4506 1740 48
Jefferson AR 6 15 1.0 1319 1870 25
Faulkner AR 7 18 1.0 1200 980 22
Crittenden AR 9 21 1.0 1187 2420 18
Washington AR 1 23 0.7 5963 2610 48
Pope AR 8 27 0.8 1187 1870 22

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1463 seconds to compute.
2020-08-02 08:56:13

version history

Today is 2020-08-02.
74 days ago: Multiple states.
66 days ago: \(R_e\) computation.
63 days ago: created color coding for \(R_e\) plots.
58 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
58 days ago: “persistence” time evolution.
51 days ago: “In control” mapping.
51 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
43 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
38 days ago: Added Per Capita US Map.
36 days ago: Deprecated national map.
32 days ago: added state “Hot 10” analysis.
27 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
25 days ago: added per capita disease and mortaility to state-level analysis.
13 days ago: changed to county boundarieson national map for per capita disease.
8 days ago: corrected factor of two error in death trend data.
4 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.